Outcome measurement of cognitive impairment and dementia in serious digital games: a scoping review
Bibliographic record
Abstract
Purpose Dementia prevalence is increasing worldwide. With the emergence of digital rehabilitation, serious digital games are a potential tool to maintain and monitor function in people living with dementia. It is unclear however whether games can measure changes in cognition. We conducted a scoping review to identify the types of outcomes measured in studies of serious digital games for people with dementia and cognitive impairment.Methods We included primary research of any design including adults with cognitive impairment arising from dementia or another health condition; reported data about use of serious digital games; and included any cognitive outcome. We searched Medline (via EBSCO), PsycInfo, CINAHL, Web of Science, from inception to 4th March 2024 and extracted study characteristics.Results We reviewed 5899 titles, including 25 full text studies. We found heterogeneity in domains and measures used: global cognition (n = 15), specific cognitive processes (n = 13), motor function (n = 5), mood (n = 6), activities of daily living (n = 5), physiological processes (n = 4) and quality of life (n = 2). Use of outcome measurement tools was inconsistent; the most frequently used measures were the Montreal Cognitive Assessment (n = 8), the Mini-Mental State Examination (n = 7), and the Trail Making Test (n = 7). Nine studies used in-game measures, most of which were related to game performance.Conclusion We found very few studies with assessment of cognition within the game. Studies of serious games for people with dementia and cognitive impairment should develop digital outcome tools based on recommendations in Core Outcome Sets, to increase consistency between studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".